Identification of the Selective Sites for Electrochemical Reduction of CO to C_(2+) Products on Copper Nanoparticles by Combining Reactive Force Fields, Density Functional Theory, and Machine Learning

Recent experiments have shown that CO reduction on oxide derived Cu nanoparticles (NP) are highly selective toward C_(2+) products. However, understanding of the active sites on such NPs is limited, because the NPs have ∼200 000 atoms with more than 10 000 surface sites, far too many for direct quantum mechanical calculations and experimental identifications. We show here how to overcome the computational limitation by combining multiple levels of theoretical computations with machine learning. This approach allows us to map the machine learned CO adsorption energies on the surface of the copper nanoparticle to construct the active site visualization (ASV). Furthermore, we identify the structural criteria for optimizing selective reduction by predicting the reaction energies of the potential determining step, ΔE_(OCCOH), for the C_(2+) product. Based on this structural criterion, we design a new periodic copper structure for CO reduction with a theoretical faradaic efficiency of 97%.